The present invention relates to the field of knowledge management, and in particular to the field of organization of documents in a community space over time by controlling document aging information in a document management system.
A document management system generally is a document repository that users can access to add or retrieve a given document. A recommender system is a well-known form of a document management system and embodies the concept of knowledge sharing in communities of people by organizing the community work around documents relevant to specific topics or subjects. Some recommender systems are even able to provide personalized recommendations that take into account similarities between people based on their user profiles.
An example of a recommender system is Knowledge Pump developed by Xerox Corporation. Knowledge Pump provides users with personalized recommendations for things to read. When users sign up, they join communities of people with similar interests. Profiler agents track and map each user's interests, learning more about the person each time (s)he uses Knowledge Pump. A recommender agent finds matches between new items and user preferences, automatically sending relevant information to people as it is found. For more background concerning the Knowledge Pump, reference is made to the article “Making Recommender Systems Work for Organizations” by Glance et al., published in the Proceedings of PAAM, 1999.
Typically, knowledge sharing in a community system is focused on the filtering and the recommendation of incoming documents to users. The decision on recommendation is generally based on content or collaborative (i.e., social) filtering. These two methods of filtering are complementary. The content filtering method is based on the analysis of document content and the evaluation of document similarity (i.e., methods relating to information retrieval). In contrast, the collaborative filtering method is based on existing user ratings for documents and deriving user interest correlations in order to predict user ratings for future documents.
While both filtering methods address the problem of input document filtering, systems are not known to help users determine how long to retain documents after making recommendations (i.e., inside the community or user working space). That is, when the number of documents in a community grows, a recommender system faces the problem of “ecology”, documents once recommended and referred to by others may keep or lose their value over time, thus gradually transforming the community into a collection of loosely-related documents, with many that may be irrelevant, obsolete or outdated.
Also, while techniques for enriching documents with additional associations are also known, it is believed they are single-user oriented and do not concern community ecology. A typical example is “Haystack” which adapts to a user by using automated data gathering through active observation of user activity to customize a single user's information collection and to adapt to individual query needs of the user. More details concerning Haystack are disclosed in the article “Haystack: Per-User Information Environments”, by E. Adar, D. Karger and L. A. Stein, In Proc. of CIKM 1999, Nov. 2–6, Kansas City, Mo.
In order to provide an improved quality of services delivered to users, it would be advantageous to provide community support systems that organize documents better than simple collections, by enriching documents therein with a set of additional associations (e.g., semantic, temporal, etc.). It would further be advantageous if these additional associations could then be used to improve the prediction of document aging information in collaborative information systems such as Knowledge Pump.